'queue' 검색 결과

Prepare overdue invoice reminders using accounting records and recent payment data. Adjust each message to the customer's payment history, flag possible payments for checking, and send through PayPal or queue email drafts after approval.

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• Checking which customers have overdue invoices
• Adjusting reminders to each customer's payment history
• Checking for recent payments before sending reminders

Assign unowned leads and opportunities using company routing rules and existing account relationships. Prepare a recommendation with its reasoning, draft handoff notes, and apply ownership changes after a person accepts or overrides each recommendation.

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• Assigning newly received leads to sales representatives
• Reviewing a queue of unassigned opportunities
• Preparing handoff notes with assignment reasons

Keep customer records and sales deals current using emails, meetings, and call transcripts. Prepare activity summaries, follow-up drafts for quiet deals, cleanup proposals, and a next-step queue with owners and dates.

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• Turn sales calls into structured deal notes
• Draft follow-ups for quiet deals
• Review duplicate contacts and outdated records

Generate Terraform alerts for Cloud Run services, jobs, and worker pools using PromQL, a monitoring query language. Monitor errors, latency, resource usage, job failures, and queue delays, with configurable thresholds and explanations of when each alert triggers.

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• Monitor Cloud Run service errors and latency
• Detect failed batch job executions
• Monitor worker pool backlogs and message age

Retrieve BigQuery execution records and resource metrics using the right tool for each task. The skill guides monitoring tool selection and SQL queries for performance investigations, capacity analysis, and reviews of billable compute and storage usage.

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• Retrieve execution details for a slow job
• Check compute capacity contention and queue delays
• Review reserved capacity and autoscaling limits

Process one recommendation selected by ID from a local issue or CI triage report. Apply, reject, or defer it, record the outcome, and obtain separate confirmation before each change made on GitHub.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Apply one recommendation selected by its item ID
• Reject a recommendation and record the reason
• Defer a decision for the next queue review

Build Laravel applications with authenticated APIs, interactive interfaces, and background jobs. The skill guides database relationships, query optimization, and feature tests, using tools such as Sanctum for authentication and Livewire for interfaces.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an authenticated Laravel API
• Optimize database queries and relationships
• Set up background jobs with Horizon

Review open issues and discussions to create a clear work queue. Record the five highest-priority items, suggested labels, possible duplicates, and cases needing human judgment in a state file for future reviews.

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• Prioritizing a backlog of open issues
• Finding possible duplicate reports
• Preparing label suggestions

Set up an OpenRig team for ongoing repository work, with clear ownership, implementation, independent review, and handoffs. Choose direct coordination, a task queue, or an explicit workflow, then expand the team as authorized work requires.

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• Starting a builder and reviewer team for a repository
• Keeping ownership visible across task handoffs
• Choosing direct work, a queue, or a workflow

Look up exact syntax, options, JSON output, defaults, and error meanings for a known OpenRig command. Use the reference to check command behavior and diagnose runtime differences against the installed help and running daemon.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking required flags for a known rig command
• Looking up queue commands and handoff syntax
• Investigating command errors and daemon compatibility

Coordinate work across an OpenRig agent team with clear assignments, attached context, completion criteria, and selected review checkpoints. Track queue status, identify blocked or idle owners, and resolve handoff gaps while leaving implementation with the assigned agents.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assign agent tasks with context and completion criteria
• Identify blockers and missed handoffs from queue status
• Route reviews at the agreed milestones

Develop and deploy Cloudflare Workers, manage related storage and services, and troubleshoot configuration with Wrangler. The skill checks official documentation and configuration definitions before preparing commands, with guidance for local development, environments, secrets, and logs.

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• Develop and deploy a Cloudflare Worker
• Configure KV, R2, and D1 resources
• Separate staging and production settings

Model ongoing requests and multi-step agent work to estimate capacity, latency, and cost per completed task. Break execution into stages, account for queues and retries, and produce workload tables with average and peak resource budgets.

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• Estimate capacity for ongoing AI requests
• Analyze queues and tool waits in agent workflows
• Compare resource costs per completed task

Review pull requests at a specific commit using existing discussions, review plans, and executed checks. Produce a detailed Chinese review and a concise English verdict, then publish and read back the result to confirm its contents.

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• Review a named pull request at a specific commit
• Review a queue filtered by state or time period
• Audit changes against maintainer guidance

Find the relevant architectural guidance for API design, security, cloud infrastructure, message queues, and caching. This skill acts as an index to specialized references and lists principles for structuring software, distributed systems, and access controls.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding guidance for API design
• Reviewing security architecture topics
• Exploring containers, K8s, and Serverless

Add OpenTelemetry tracing to frontend and backend flows. Connect execution records across services and background jobs, record phase durations and entity IDs, and mark failures so developers can investigate slow or broken operations.

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• Tracing a user action across multiple services
• Investigating slow queues and background jobs
• Connecting missing or disconnected traces

Review a configured queue of pull requests, checking code changes, linked issues, tests, and review discussions. Report actionable findings and supporting evidence, then assess approval or merge separately against the permissions and conditions configured for each action.

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• Triage a filtered pull request queue manually or on a schedule
• Report actionable findings with locations and concrete fixes
• Assess approval and merge against separate policies

Deploy teams of AI agents on Flow Nexus and automate development, research, or data processing workflows. Define step dependencies and event triggers, assign suitable agents, and monitor progress, queues, and execution history.

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• Automating testing, builds, and deployment
• Distributing research and analysis across agents
• Scheduling data extraction, transformation, and loading

Implement one selected GitHub issue within a freshly confirmed human scope. Check ownership and existing pull requests, reproduce bugs when relevant, follow the implementation procedure, and open at most one pull request for the change.

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• Implement an accepted feature or documentation change
• Reproduce and fix a reported bug
• Check for an existing pull request before starting

Organize pull request reviews by selecting eligible labeled or explicitly named requests and distributing them to a dedicated worker pool. It records selection decisions and limits concurrent reviews, with two worker sessions by default.

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• Reviewing a selected list of pull requests
• Processing a labeled review queue with limited concurrency
• Checking code owner conditions before scheduling reviews

Use this skill when the user is doing hands-on NVMe-over-Fabrics storage-target work on a BlueField DPU or ConnectX NIC with DOCA STA — standing up a doca_sta DOCA Core context that accelerates the target-side NVMe-oF data path over RDMA, defining doca_sta_subsystem targets (NQN + namespaces) backed by local NVMe-PCI backend disks (doca_sta_be), checking device support via doca_sta_cap_is_supported, sizing the per-connection I/O queues, or debugging DOCA_ERROR_* from a STA call. Trigger even when the user does not say "DOCA STA" — typical implicit phrasings include "my NVMe-oF Connect never completes", "Identify Controller times out over RoCE", "16 I/O queues at depth 1024 — does this BlueField support that", "offload the nvmf target onto the DPU", or "DOCA_ERROR_IO_FAILED on an NVMe read". Refuse and route elsewhere for DOCA install, raw RDMA data movement, raw packet I/O, flow-rule programming, or initiator-side / host NVMe stack work — those belong to other skills.

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Operate the DOCA UROM Service container on BlueField Arm for remote memory operations (puts, gets, atomics, collectives) enqueued by a paired host using `doca-urom`: pull the NGC image, choose the UCX component, size queues, configure Comch pairing, and align host and service versions. SECURITY: the service has no standalone access control; Comch pairing and RDMA permissions are the boundary. Pair only intended hosts, expose least-privilege memory regions, and verify both views before start. Trigger for slow UCX collectives, unexpected NOT_PERMITTED, or missing completions. Do not use for host application code, MPI/UCX integration design, or DOCA install.

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DOCA Rivermax (doca_rmax) receive streams for timing-precise media over IP (SMPTE ST 2110 video / audio, market data, scientific feeds) on a BlueField DPU or ConnectX host, once the Rivermax SDK and its license are installed; getting the SDK or license is doca-setup / doca-public-knowledge-map territory. Trigger on "ST 2110 receive isn't getting frames", "sub-microsecond jitter on my media receiver", "NOT_SUPPORTED from doca_rmax_init", "no recv events after stream start", or "license check failing on a media receiver", even without naming Rivermax. Covers doca_rmax_in_stream sessions, the SDK + license precondition check, doca_rmax_get_*_supported capability queries, pairing with doca-eth queues and doca-flow steering, and DOCA_ERROR_* from Rivermax calls. Refuse and route elsewhere for programming the underlying queue (doca-eth), steering rules (doca-flow), PTP / clock sync (doca-firefly), or best-effort packet I/O.

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Use this skill for hands-on DOCA GPI programming — wiring a GPU-Packet-Initiator context so a CUDA kernel drives RDMA queues directly from GPU memory without host CPU mediation. Covers picking GPI vs doca-gpunetio, the doca_gpi / domain / channel object model, the GPU-side handle handoff (doca_gpu_gpi_channel*), attaching GPU memory to a GPI domain, the domain and channel attribute objects, and debugging DOCA_ERROR_* from doca_gpi_* calls. Trigger even when the user does not explicitly mention "DOCA GPI" — implicit phrasings include "my CUDA kernel needs to post RDMA directly from GPU memory", "DOCA_ERROR_* from doca_gpi_gpu_channel_get", "how do I hand a GPU handle to my CUDA kernel", "how many channels can a GPI domain hold", or "GPU kernel driving RDMA without the host CPU on the path". Refuse and route elsewhere for the doca-gpunetio Send/Receive surface, the doca-rdma queue lifecycle, DPA-side initiation (doca-rdmi), or the CUDA programming model — those belong to other skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.